Semiconductor processing tools are highly complex devices. Their performance in real time is typically evaluated by monitoring values of a large number of tool sensors that reflect operational parameters such as temperature, pressure, and power. This tool evaluation may be conducted by comparing empirically measured sensor values to values predicted by a model.
An example of a model for predicting the behavior of a semiconductor processing tool is the Universal Process Modeling (UPM) technique. This model was developed by Triant Technologies, Inc., of Nanaimo, British Columbia.
FIG. 11 is a simplified schematic diagram illustrating the UPM technique. As shown in step A of FIG. 11, semiconductor processing tool 1101 is operated, and input vector 1102 comprising values 1104 for tool sensors 1106 is sampled at a time during the tool run (step A). Sampled input vector 1102 is then compared with reference data library 1112 (step B). Reference data library 1112 represents a compilation of vectors 1150-1157 from previous normal operation of semiconductor processing tool 1101. Vectors 1150-1157 of reference library 1112 include a value for each sensor of input vector 1102.
As a result of the comparison of step B, vector subset 1110 comprising vectors 1150, 1153, 1156, and 1157 is compiled from reference data library 1112 utilizing a nearest neighbor selection process between input vector 1102 and the vectors of reference data library 1112 (step C). Vectors 1150, 1153, 1156, and 1157 of vector subset 1110 reflect sensor values of previous normal operation of tool 1101 that most closely resemble input vector 1102. A variety of techniques may be employed in the nearest neighbor selection process as known to those of skill in the art. Precise details of the nearest neighbor section process utilized by the UPM model are proprietary.
Next, vectors 1150, 1153, 1156, and 1157 of subset 1110 are combined to produce a single output prediction vector 1116 (step D). Output prediction vector 1116 reflects the state of semiconductor processing tool 1101 in relation to previous normal operation. Output prediction vector 1116 may be communicated to the tool operator in several ways. For example, as shown in FIG. 11, values 1118 of individual sensors 1120 of output prediction vector 1116 may be combined to produce a single fault detection index 1114 that reflects the values of all of the tool sensors (step E). Alternatively, as shown in FIG. 12, values 1118 representing each individual sensor 1120 of the output prediction vector may be plotted along spokes 1202 of “bull's eye” graph 1200, with radial distance 1204 representing deviation of the measured sensor value from expected values.
One aspect of the UPM modeling technique just described is that it does not consider possible correlation between groups of related sensor values, such as related tool pressures, related tool temperatures, or related tool powers. Rather, all sensors are accorded equal weight in generating the fault detection index. This approach thus does not include potentially valuable correlation between related operational parameters that could provide more reliable fault detection information.
Moreover, while the bull's eye graph of FIG. 12 provides the tool operator with an organized presentation of real-time tool operational parameters, the operator must still continuously monitor each of the tool sensors in order to detect a fault. Doing this for a large number of sensors may occupy the operator's attention, diverting him or her from other important tool management tasks.
Another aspect of the modeling technique shown in FIG. 11 is that selection of nearest neighbor vectors to form the vector subset and the output prediction vector is based solely upon the sensor values. Other potentially relevant information, for example the time during the tool run at which the input vector is sampled, is not taken into account in the nearest neighbor selection process. This may affect the model's accuracy where the input vector and the library vector are similar merely by chance, for example where a temperature component of the input vector is measured at an early stage (ramp up) of a tool run, while the temperature component of the library vector is measured at a late stage (ramp down) of a tool run. In such a case values of the temperature component of the input and library vectors may be similar by chance, but the library vector is not otherwise an accurate prediction of the input vector.
Accordingly, more sophisticated techniques for fault detection of semiconductor processing tools are desirable.